Tutorial: Advancing Brain-Computer Interfaces with Generative AI for Text, Vision, and Beyond

Title of the Tutorial: Advancing Brain-Computer Interfaces with Generative AI for Text, Vision, and Beyond

Tutorial ID: TH12 

Speakers: Shreya Shukla, Jose Torres, Jacek Gwizdka, Abhijit Mishra, Shounak Roychowdhury

Date: February 26, 2025

Slot: 8:30 AM – 12:30PM 

Venue: Pennsylvania Convention Center,  Philadelphia, Pennsylvania

Program and Registration Details: https://aaai.org/conference/aaai/aaai-25/tutorial-and-lab-list/  

Tutorial Overview

In this half day tutorial we explore the transformative intersection of Brain-Computer Interface (BCI) neurotechnology and Generative AI, focusing on how brain activity can be decoded into coherent language, imagery, and actions through advanced deep learning techniques. By integrating brain signal data such as EEG, MEG, and fMRI with multimodal AI models like Large Language Models (LLMs) and Generative Adversarial Networks (GANs), participants will gain insights into cutting-edge methods for brainwave pattern recognition and data fusion. Targeted at researchers, practitioners, and students, this session offers a deep dive into the applications of BCI-driven AI in fields such as accessibility, augmented and virtual reality, and cognitive psychology, highlighting both the progress and challenges in this rapidly evolving area.

Objectives

1. Introduce key brain-computer interface concepts and their applications in generative AI.

2. Demonstrate the integration of neurotechnology and multimodal AI models for text, image, and speech generation.

3. Discuss current trends and research challenges in applying BCI data for AI-driven synthesis across multiple modalities.

Learning Outcomes

By the end of this tutorial, participants will be able to:

• Understand core BCI signals and their applications in generative AI for text, vision, and multimodal synthesis.

• Identify and evaluate pattern recognition techniques for translating brain signals into language and images.

• Explore advanced AI models, including LLMs and GANs, in the context of BCI applications.

• Discuss real-world applications and ethical considerations related to AI-driven neurotechnology.

Gain familiarity with foundational frameworks and methodologies for BCI and multimodal AI research.